AI Ops

AI Ops is the discipline of deploying, securing, monitoring, and operating AI systems in production — the same way DevOps does for traditional software. It covers infrastructure setup, AI model deployment, LLM configuration, security and access, monitoring and incident response, and safeguards against prompt injection, jailbreaks, and policy violations. We deliver all of it as a managed engagement in your own infrastructure.

Calling a model API gets a prototype working. It doesn't provide access control, per-team usage limits, monitoring, incident response, prompt-injection defenses, audit logs, or compliance configuration — all of which production systems need once real users and real data are involved. AI Ops is the operational layer around the model: the parts that determine whether it keeps running reliably once it leaves a developer's laptop.

Yes. We deploy on AWS, Azure, GCP, or fully on-prem, including air-gapped environments with no internet access. For on-prem or air-gapped deployments we work with self-hosted models — Llama, Mistral, or custom fine-tunes — provisioned on Kubernetes with your GPU/VRAM constraints factored into the setup. You own the environment; we configure and operate within it.

We configure output filtering and content policies, enforce per-role access to models and data, and set up detection for prompt-injection patterns — including indirect injection via uploaded documents or connected data sources. This sits alongside standard access controls: SSO (SAML/OIDC), MFA, RBAC, and audit logging, so every model interaction is attributable and every anomalous pattern is visible before it becomes an incident.

We configure deployments to align with GDPR, HIPAA, and PCI requirements — including data residency, PII/PHI/PCI scoping, and audit logging. Where you already run a SIEM, we integrate AI monitoring events into it. Compliance requirements are captured during intake, before any infrastructure work begins.

Yes. For clients who need to keep data out of external APIs — for cost, compliance, or data-residency reasons — we deploy self-hosted models such as Llama, Mistral, or custom fine-tunes on your own infrastructure, with inference optimization and region setup handled as part of the deployment. We also support hybrid setups that route between a self-hosted model and a cloud API as a fallback.

That's what Monitoring & Ops and the AI Operations Retainer exist for. We set up latency, error, and token-usage alerting, and provide incident response with on-call support — so an outage is caught by monitoring, not reported by a customer. Multi-model fallback can also be configured, so traffic reroutes automatically if a model is down, degraded, or too expensive to keep using.

We offer three models: Assessment + Deployment is a short-term engagement (2–8 weeks) to audit the current setup, deploy to production, and hand over to your internal team. The AI Operations Retainer is an ongoing monthly engagement to monitor, maintain, and optimize AI already in production. Full Cycle Partnership combines both as a long-term engagement with a dedicated ops team. We'll recommend which fits on the intake call.

You fill a structured intake form covering contacts and timeline, deployment target, auth/repo/security requirements, and LLM/inference needs. We use that to scope effort, roles, and timeline before any work begins — so there are no surprises once the engagement starts. Actual start dates depend on deployment complexity and specialist availability, confirmed during scoping.

Not necessarily — this is one of the most common ways clients engage us. We start with an assessment of the current setup: where it's deployed, what monitoring exists (if any), what security and access controls are in place, and where the reliability gaps are. From there we scope the specific fixes needed rather than assuming a ground-up rebuild is required.

The terms overlap but emphasize different things. MLOps traditionally covers the lifecycle of training and retraining machine learning models. LLMOps is a narrower term specifically about operating large language models — prompting, fine-tuning, evaluation. AI Ops, as we use it, is the broadest and most practical term for companies: it covers everything needed to run any AI system reliably in production — infrastructure, model deployment, security, monitoring, and safeguards — regardless of whether the underlying model is a hosted LLM API, a self-hosted open-weight model, or a custom ML pipeline.

Both. Startups typically come to us with a pilot stuck in staging and need an Assessment + Deployment engagement to get it into production safely and quickly. Larger and regulated companies more often need the AI Operations Retainer or Full Cycle Partnership, with heavier emphasis on compliance, access control, and on-call coverage. The engagement model scales to the company's size and risk profile — we scope it on the intake call rather than applying a one-size-fits-all package.